Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Many of the important services running on data centres are latency-critical, time-varying, and
demand strict user satisfaction. Stringent tail-latency targets for colocated services and …

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model
is trained with updates computed on mobile devices while keeping the data of users local …

Modern software applications are increasingly configurable, which puts a burden on users to
tune these configurations for their target hardware and workloads. To help users, machine …

Machine learning has enabled significant benefits in diverse fields, but, with a few
exceptions, has had limited impact on computer architecture. Recent work, however, has …

One of the most critical aspects of integrating loosely-coupled accelerators in
heterogeneous SoC architectures is orchestrating their interactions with the memory …

Existing approaches to distribute Generative Adversarial Networks (GANs) either (i) fail to
scale for they typically put the two components of a GAN (the generator and the …

Multi-chip many-core neural network systems are capable of providing high parallelism
benefited from decentralized execution, and they can be scaled to very large systems with …

Hardware performance counters (HPCs) that measure low-level architectural and
microarchitectural events provide dynamic contextual information about the state of the …